Title:

Multiple-Valued Logic and Machine Learning in Multi-State System Reliability Analysis

Keynote speaker:

Professor Elena Zaitseva, University of Zilina, the Slovak Republic 

Professor Elena Zaitseva works at the Department of Informatics of the University of Zilina, the Slovak Republic. Her research interests include mathematical methods in reliability and safety analysis, classification problems, and algebra logic-based methods application in reliability evaluation of complex systems. She is the author of more than 100 articles. She has led international and national projects thematically related to reliability analysis and its use in applications such as information technology, healthcare and ecology. She is a member of the Technical Committee of the European Safety and Reliability Association and the Chair of the Reliability Association Chapter of Czechoslovakia Section of IEEE.

Abstract:

Multi-State System is one of the mathematical models used in reliability engineering.  This model allows us to determine quantitative characteristics to evaluate the behavior of the original system. Typically, such a mathematical model approximates the behavior of the initial system and contains some uncertainty. However, this uncertainty can significantly increase if the initial data for building the model is uncertain and incompletely specified. The uncertainty of the mathematical model also leads to incorrect estimates of the system’s behavior and its reliability. Therefore, it is important to develop methods that account for the uncertain nature of the initial data, particularly epistemic uncertainty. Machine learning methods, especially classification, can be effective in developing a mathematical model of a Multi-State System (MSS) based on incompletely specified and uncertain data.